Journal articles: 'Conjoint analysis (Marketing)'

1

Martin, John, and Thomas E. Moore. "Conjoint Analysis:." Journal of Marketing for Higher Education 4, no. 1-2 (July 22, 1993): 379–403. http://dx.doi.org/10.1300/j050v04n01_26.

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Vriens, Marco. "Solving marketing problems with conjoint analysis∗." Journal of Marketing Management 10, no. 1-3 (April 1994): 37–55. http://dx.doi.org/10.1080/0267257x.1994.9964259.

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Rao, Vithala R., and Luis Eduardo Pilli. "Conjoint Analysis para Pesquisa de Marketing no Brasil." Revista Brasileira de Marketing 13, no. 4 (September 11, 2014): 25–38. http://dx.doi.org/10.5585/remark.v13i4.2707.

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Este artigo oferece uma reviso, de 1971 at a atualidade, dos mtodos de conjoint analysis que so abordagens de coleta de dados baseadas em preferncias ou escolhas declaradas pelos consumidores. Milhares de estudos foram realizados com o uso de conjoint analysis, desde a introduo do mtodo no incio da dcada de 70. Este conjunto de mtodos permite que os pesquisadores de mercado estudem trade-off entre os atributos de novos produtos, sendo til para vrias decises de marketing com design de produto, apreamento e segmentao de mercado. O conjunto atual de opes de conjoint analysis composto pela abordagem tradicional de preferncia declarada, pelas tcnicas de escolhas discretas (CBCA ou choice based conjoint analysis) que se baseiam em escolhas declaradas, pela abordagem autoexplicativa que usa elicitao direta de importncia de atributos e avaliao dos nveis dos atributos e pela abordagem adaptativa (ACA ou adaptive conjoint analysis) que implica em coleta de dados por etapas e adaptativa. Este artigo resume estes mtodos e seus desenvolvimentos recentes e apresenta uma aplicao no Mercado brasileiro. Dada a versatilidade do mtodo, existe um enorme potencial para a pesquisa de marketing no Brasil. Essencialmente, esta metodologia est viva e crescendo.

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Arora, Raj. "Formulating direct marketing offers with conjoint analysis." Journal of Direct Marketing 5, no. 1 (1991): 48–56. http://dx.doi.org/10.1002/dir.4000050108.

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Beall, Ron, and Leslie W. Perttula. "Conjoint Analysis: A Pedagogical Model." Journal of Marketing Education 13, no. 3 (December 1991): 76–82. http://dx.doi.org/10.1177/027347539101300309.

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Dubas, Khalid M., and James T. Strong. "Course Design Using Conjoint Analysis." Journal of Marketing Education 15, no. 1 (April 1993): 31–36. http://dx.doi.org/10.1177/027347539301500105.

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Fletcher, Keith. "An Analysis of Choice Criteria Using Conjoint Analysis." European Journal of Marketing 22, no. 9 (September 1988): 25–33. http://dx.doi.org/10.1108/eum0000000005298.

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Kim, Dong Soo, Roger A. Bailey, Nino Hardt, and Greg M. Allenby. "Benefit-Based Conjoint Analysis." Marketing Science 36, no. 1 (January 2017): 54–69. http://dx.doi.org/10.1287/mksc.2016.1003.

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Christian Zinkhan, F., and George M. Zinkhan. "Using Conjoint Analysis to Design Financial Services." International Journal of Bank Marketing 8, no. 1 (January 1990): 31–34. http://dx.doi.org/10.1108/02652329010136389.

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Mandloi, Anita. "Conjoint Analysis and its Applications in Marketing Research." International Journal of Mathematics Trends and Technology 68, no. 3 (March 25, 2022): 43–44. http://dx.doi.org/10.14445/22315373/ijmtt-v68i3p508.

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Mandloi, Anita. "Conjoint Analysis and its Applications in Marketing Research." International Journal of Mathematics Trends and Technology 68, no. 3 (March 25, 2022): 43–44. http://dx.doi.org/10.14445/22315373/ijmtt-v68i3p508.

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Garcia, Rosanna, Paul Rummel, and John Hauser. "Validating agent-based marketing models through conjoint analysis." Journal of Business Research 60, no. 8 (August 2007): 848–57. http://dx.doi.org/10.1016/j.jbusres.2007.02.007.

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Heinonen, Jarmo. "Analysing E-Services and Mobile Applications with Companied Conjoint Analysis and fMRI Technique." International Journal of E-Services and Mobile Applications 7, no. 4 (October 2015): 57–72. http://dx.doi.org/10.4018/ijesma.2015100105.

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Previous research has shown that neuromarketing and conjoint analysis have been used in many areas of consumer research, and to provide for further understanding of consumer behaviour. Together these two methods may reveal more information about hidden desires, expectations and restrains of consumers' brain. This paper attempts to examine these two research methods together as a companied analysis. More specifically this study utilizes fMRI and conjoint analysis as a tool for analysing consumer's preferences and decision making in e-health and ITC products. This paper provides theoretical background with short history of conjoint analysis and contributions for the audience of consumer research: 1. how conjoint evaluation models works; 2. different conjoint models; 3. counting attribute interactions in conjoint analysis; and 4. brain activation triggers in fMRI and connection to conjoint analysis. Researchers of consumer behaviour may learn a new method for understanding user´s preferences and decision making from e-services and mobile applications. E-services and mobile applications need to be successfully analysed for various marketing segments of new products. An application might appeal well to consumer, but what is known about the attributes that makes consumer act? The customer might orally request other than her brain will inform. Needs could be derived from product parts or attribute bundles of mobile applications. The knowledge will help the producer to target new applications to relevant marketing segments.

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Bridges, John F. P., Sarah C. Searle, Frederic W. Selck, and Neil A. Martinson. "Engaging Families in the Choice of Social Marketing Strategies for Male Circumcision Services in Johannesburg, South Africa." Social Marketing Quarterly 16, no. 3 (August 26, 2010): 60–76. http://dx.doi.org/10.1080/15245004.2010.500443.

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Male circumcision (MC) prevents HIV acquisition in males, leading to calls for extensive implementation in sub-Saharan Africa. The widespread adoption of male circumcision will require social marketing targeted at various families and family members. The objective of this article is to demonstrate the utility of conjoint analysis in the choice of social marketing strategies tailored to different populations to promote male circumcision in Johannesburg, South Africa. Seven social marketing strategies for MC were identified through open-ended interviews ( n = 25). Preferences were assessed using conjoint analysis, implemented in a cross-sectional survey of randomly selected households. An oversampling strategy ensured balance between Blacks (34%), Coloreds (mixed-race; 32%), and Whites (34%). Respondents randomly received a block of 4 conjoint analysis tasks comparing 2 mutually exclusive and exhaustive subsets of the 7 strategies. Preferences were then evaluated using logistic regression stratified by ethnicity and family member. Whereas all strategies were attractive, television marketing, endorsement by church/school leaders, and a countrywide program were most preferred ( p < .0001). Stratified analyses identified heterogeneity, e.g., only Coloreds valued radio ( p < .0001) and a lasting presence in the community ( p < .0001). Within families, mothers and sons were most concordant for Blacks ( p < .05) and Coloreds ( p < .01), but fathers were most concordant with sons among Whites ( p <.05). Conjoint analysis provides valuable insight into preferences and can be used in the development of social marketing, especially when aimed at promoting behavioral change.

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Green, Paul E., and Abba M. Krieger. "Segmenting Markets with Conjoint Analysis." Journal of Marketing 55, no. 4 (October 1991): 20. http://dx.doi.org/10.2307/1251954.

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Green, Paul E., and Abba M. Krieger. "Segmenting Markets with Conjoint Analysis." Journal of Marketing 55, no. 4 (October 1991): 20–31. http://dx.doi.org/10.1177/002224299105500402.

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Reibstein, David, John E. G. Bateson, and William Boulding. "Conjoint Analysis Reliability: Empirical Findings." Marketing Science 7, no. 3 (August 1988): 271–86. http://dx.doi.org/10.1287/mksc.7.3.271.

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Gan, Christopher, and E. Jane Luzar. "A Conjoint Analysis of Waterfowl Hunting in Louisiana." Journal of Agricultural and Applied Economics 25, no. 2 (December 1993): 36–45. http://dx.doi.org/10.1017/s1074070800018940.

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AbstractConjoint analysis, widely used in marketing research, offers an alternative resource valuation approach suited to outdoor recreation activities characterized as multiattribute. Design, implementation, and interpretation of conjoint analysis are reviewed in the context of recreation applications. Conjoint analysis is used in an analysis of waterfowl hunting in Louisiana. Using primary data collected from a survey of waterfowl hunters, ordered logit is used to estimate willingness-to-pay for recreation experience attributes.

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Green, Paul E. "CONSURV: Conjoint Analysis Software." Journal of Marketing Research 29, no. 3 (August 1992): 387. http://dx.doi.org/10.2307/3172754.

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Ding, Min, Rajdeep Grewal, and John Liechty. "Incentive-Aligned Conjoint Analysis." Journal of Marketing Research 42, no. 1 (February 2005): 67–82. http://dx.doi.org/10.1509/jmkr.42.1.67.56890.

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Because most conjoint studies are conducted in hypothetical situations with no consumption consequences for the participants, the extent to which the studies are able to uncover “true” consumer preference structures is questionable. Experimental economics literature, with its emphasis on incentive alignment and hypothetical bias, suggests that more realistic incentive-aligned studies result in stronger out-of-sample predictive performance of actual purchase behaviors and provide better estimates of consumer preference structures than do hypothetical studies. To test this hypothesis, the authors design an experiment with conventional (hypothetical) conditions and parallel incentive-aligned counterparts. Using Chinese dinner specials as the context, the authors conduct a field experiment in a Chinese restaurant during dinnertime. The results provide strong evidence in favor of incentive-aligned choice conjoint analysis, in that incentive-aligned choice conjoint outperforms hypothetical choice conjoint in out-of-sample predictions. To determine the robustness of the results, the authors conduct a second study that uses snacks as the context and considers only the choice treatments. This study confirms the results by providing strong evidence in favor of incentive-aligned choice analysis in out-of-sample predictions. The results provide a strong motivation for conjoint practitioners to consider conducting studies in realistic settings using incentive structures that require participants to “live with” their decisions.

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Arora, Amit Kumar, and Vijay Prakash Gupta. "Conjoint Analysis of Consumers' Preference towards Packaged Milk." Indian Journal of Marketing 50, no. 12 (December 31, 2020): 40. http://dx.doi.org/10.17010/ijom/2020/v50/i12/156308.

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Jaeger, Sara R., Duncan Hedderley, and Halliday J. H. MacFie. "Methodological issues in conjoint analysis: a case study." European Journal of Marketing 35, no. 11/12 (December 2001): 1217–39. http://dx.doi.org/10.1108/eum0000000006474.

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Dobney, Saul, Carlos Ochoa, and Melanie Revilla. "More Realism in Conjoint Analysis." International Journal of Market Research 59, no. 4 (July 2017): 495–516. http://dx.doi.org/10.2501/ijmr-2017-037.

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The main goal of this research is to study the impact on the answers and data quality of making conjoint questions more realistic by introducing some randomised noise into the descriptions of the conjoint levels or by simulating the way an e-commerce website displays products. Conjoint analysis is an advanced market research technique commonly used to estimate preference share for products and services with different attributes and levels. A common criticism of it is in regard to the repetitive nature of the questions. In order to study this, an experiment was implemented in Spain using 1,600 respondents from the opt-in online panel Netquest. The respondents were randomly assigned to one of the following four conditions: classic conjoint design without noise (control group); classic conjoint design with some random textual and numerical noise added to the attribute level descriptions; conjoint simulating e-commerce display of products but no noise; and conjoint simulating e-commerce display and some random textual and numerical noise. The four groups were compared in terms of data quality, survey evaluation and substantive results. The results show a directional but not statistically significant improvement of quality of estimations. In terms of survey evaluation, even if the improvements are not systematic, there is a clear tendency for an improved evaluation when an e-commerce layout is used, but not when random noise is used. Substantive results are not affected.

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Carroll, J. Douglas, and Paul E. Green. "Psychometric Methods in Marketing Research: Part I, Conjoint Analysis." Journal of Marketing Research 32, no. 4 (November 1995): 385–91. http://dx.doi.org/10.1177/002224379503200401.

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Hagerty, Michael R. "Improving the Predictive Power of Conjoint Analysis: The use of Factor Analysis and Cluster Analysis." Journal of Marketing Research 22, no. 2 (May 1985): 168–84. http://dx.doi.org/10.1177/002224378502200206.

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A method is derived to improve the predictive accuracy of conjoint analysis by grouping respondents with similar preferences. The often-used model of cluster analysis is shown to be inadequate because real respondents do not form densely packed clusters in preference space. The author then derives the best method of weighting respondents in the sense of maximizing predictive accuracy in conjoint analysis. This method turns out to be a form of Q-type factor analysis. This “optimal weighting” method is shown to perform better than cluster analysis and individual-level analysis in Monté Carlo examples and in real data. Optimal weighting is contrasted with other methods for improving conjoint analysis, and recommendations on their use are made.

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Rzepakowski, Piotr. "Supporting telecommunication product sales by conjoint analysis." Journal of Telecommunications and Information Technology, no. 3 (June 26, 2023): 28–34. http://dx.doi.org/10.26636/jtit.2008.3.883.

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Conjoint analysis is widely used as a marketing research technique to study consumers’ product preferences and simulate customer choices. It is used in designing new products, changing or repositioning existing products, evaluating the effect of price on purchase intent, and simulating marketshare. In this work the possibility of conjoint analysis usage in telecommunication filed is analyzed. It is used to find optimal products which could be recommended to telecommunication customers. First, a decision problem is defined. Next, the conjoint analysis method and its connections with ANOVA as well as regression techniques are presented. After that, different utility functions that represent preferences for voice, SMS, MMS and other net services usage are formulated and compared. Parameters of the proposed conjoint measures are determined by regression methods running on behavioral data, represented by artificially generated call data records. Finally, users are split in homogenous groups by segmentation techniques applied to net service utilities derived from conjoint analysis. Within those groups statistical analyses are performed to create product recommendations. The results have shown that conjoint analysis can be successfully applied by telecommunication operators in the customer preference identification process. However, further analysis should be done on real data, other data sources for customer preference identification should be explored as well.

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Wittink, Dick R., and Philippe Cattin. "Commercial Use of Conjoint Analysis: An Update." Journal of Marketing 53, no. 3 (July 1989): 91–96. http://dx.doi.org/10.1177/002224298905300310.

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The authors report results of a survey conducted to update a previous one on the commercial use of conjoint analysis. They document an extensive number of applications and show systematic changes in their characteristics consistent with research results reported in the literature. Issues relevant to the options available to analysts involved in the conduct of conjoint analysis are identified and discussed.

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Pirog III, Stephen F. "Promoting Statistical Analysis in the Marketing Curriculum: A Conjoint Analysis Exercise." Marketing Education Review 20, no. 3 (October 2010): 249–54. http://dx.doi.org/10.2753/mer1052-8008200305.

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Yang, Yong. "Conjoint-Analysis on Development of Integrity Image Management in Public Sector." Advanced Materials Research 403-408 (November 2011): 414–20. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.414.

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The development of public integrity management has been the focus of public sectors, which can also be regarded as the marketing organization; the introduction of customer satisfaction and experiential marketing is a new issue. With the development of experiential marketing and “customer orientation”, the customer satisfaction has been the major content of strategic development of service industry which typically implements experiential marketing. The public sectors provide the service to the public, who is regarded as the customer in marketing; the relative measurement of satisfaction directs the quality improvement, distinctive capability and performance reinforcement for organization. This article attempts to establish index model on public satisfaction, through designing the questionnaire by orthogonal design, the conjoint analysis is executed to the current situation and suggests recommendation.

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Jedidi, Kamel, Rajeev Kohli, and Wayne S. DeSarbo. "Consideration Sets in Conjoint Analysis." Journal of Marketing Research 33, no. 3 (August 1996): 364. http://dx.doi.org/10.2307/3152132.

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Jedidi, Kamel, Rajeev Kohli, and Wayne S. Desarbo. "Consideration Sets in Conjoint Analysis." Journal of Marketing Research 33, no. 3 (August 1996): 364–72. http://dx.doi.org/10.1177/002224379603300310.

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The authors model product consideration as preceding choice in a segment-level conjoint model. They propose a latent-class tobit model to estimate cardinal, segment-level preference functions based on consumers’ preference ratings for product concepts considered worth adding to consumers’ self-explicated consideration sets. The probability with which the utility of a product profile exceeds an unobserved threshold corresponds to its consideration probability, which is assumed to be independent across product profiles and common to consumers in a segment. A market-share simulation compares the predictions of the proposed model with those obtained from an individual-level tobit model and from traditional ratings-based conjoint analysis. The authors also report simulations that assess the robustness of the proposed estimation procedure, which uses an E-M algorithm to obtain maximum likelihood parameter estimates.

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Galli, Brian J., Mohamad Amin Kaviani, Paula Steisel Goldfarb, and Ardeshir Shahmaei. "Application of Conjoint Analysis in Improving the Value of New Product Development." International Journal of Strategic Decision Sciences 8, no. 2 (April 2017): 11–30. http://dx.doi.org/10.4018/ijsds.2017040102.

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One of the most important elements of economic and business activity is developing a strategic marketing plan to improve growth prospects. One of the most important aspects of this in marketing is to understand the behavioral patterns of customers, in order to meet their needs and achieve business objectives. The consumer behavior analysis is key to establish the level of preferences expressed by the consumer towards a product or service. For this reason, conjoint analysis (CA) is one of the main models in the field of consumer research. This paper identifies the usefulness of CA as a marketing strategy in new product development (NPD). An application to “Forte Hotel Design” (presented by Lilien and Rangaswamy, 2004) is developed to highlight how CA can be an effective tool for marketing new products/services. Forte Hotels is a European hotel chain that wants to open a new facility in the United States. This research demostrates that there are multiple ways to conduct marketing research for any product or service. While the Forte Hotel case study used segmentation and targeting tools in its original analysis, the study shows that CA can also be used to analyze this marketing problem and can lead to similar decisions. This study highlights the value of CA as a tool to evaluate product attributes and shows its value in helping to make marketing decisions. It also shows that there is more than one way to dissect a marketing problem. The original case was solved using segmentation and targeting marketing tools, but this study shows that CA can be used to effectively make marketing decisions on new product development that lead to the same results/decisions as other marketing tools. This study shows original ways of using CA, to dissect a service design problem in new product development and to facilitate both product management and project management.

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Auty, Susan. "Using conjoint analysis in industrial marketing: The role of judgment." Industrial Marketing Management 24, no. 3 (June 1995): 191–206. http://dx.doi.org/10.1016/0019-8501(94)00078-b.

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Giancristofaro, Rosa Arboretti. "A New Conjoint Analysis Procedure with Application to Marketing Research." Communications in Statistics - Theory and Methods 32, no. 11 (January 9, 2003): 2271–83. http://dx.doi.org/10.1081/sta-120024480.

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Toy, Daniel, Robin Rager, and Frank Guadagnolo. "Strategic Marketing for Recreational Facilities: A Hybrid Conjoint Analysis Approach." Journal of Leisure Research 21, no. 4 (September 1989): 276–96. http://dx.doi.org/10.1080/00222216.1989.11969805.

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Voeth, Markus, Uta Herbst, and Frank Liess. "We Know Exactly what you want: The Development of a Completely Individualised Conjoint Analysis." International Journal of Market Research 55, no. 3 (May 2013): 437–58. http://dx.doi.org/10.2501/ijmr-2013-038.

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Improving the predictive validity of conjoint analysis has been an important research objective for many years. Whereas the majority of attempts have been different approaches to preference modelling, data collection or product presentation, only a few scholars have tried to improve predictive validity by individualising conjoint designs. This comes as a surprise because many markets have observed an augmented demand for customised products and highly heterogeneous customers' preferences. Against this background, the authors develop a conjoint variant based on a completely individualised conjoint design. More concretely, the new approach not only individualises the attributes, but also the attribute levels. The results of a comprehensive empirical study yield a significantly higher validity than existing standardised-level conjoint approaches. Consequently, they help marketers to gain deeper insights into their customers' preferences.

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Utari, Vani. "Analisis Conjoint pada Antarmuka Pelanggan dan Perancangan Elektronic Marketing Rumah Makan." Winners 15, no. 1 (March 31, 2014): 15. http://dx.doi.org/10.21512/tw.v15i1.676.

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This research aims to analyze 3 of 7 Steps Internet marketing and to produce market penetration strategy by developing an e-marketing-based website that is expected to help in decision making. E-marketing is a marketing strategy that can be carried out by the company with the utilization of Internet. Aie Badarun is a business entity engaged in the restaurant business that provides various menus. This research used Porter’s industry analysis, framework of seven stages of Internet marketing, conjoint analysis, and Object-Oriented Analysis and Design (OOAD). The outcome of the analysis is known that a feasible strategy for the company is leveraging the Internet as marketing media. E-marketing strategy through Restaurant Aie Badarun website is expected to be able to give customers the information needed about the product or menu of food and drink, customer testimonials, online reservation, and finding solutions to their queries about products and services of Restaurant Aie Badarun using 7C Framework which is a combination of attributes and website design. E -marketing strategy could support the overall restaurant marketing.

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Iddris, Faisal, Sana Rouis, and Mohamed Reza Shahrokhi. "Students' Preference for Online Course in Iran and Ghana: A Conjoint Analysis." International Journal of Technology and Management Research 1, no. 1 (March 12, 2020): 75–80. http://dx.doi.org/10.47127/ijtmr.v1i1.4.

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There is now intense competition among tertiary education providers due to proliferation of private and public universities offering online and offline courses. The role of marketing is becoming more important among universities as competition in the environment is getting more intense. One main approach that can facilitate the universities marketing activities is understanding what determines students' online course preference. This study aims at examining the factors that influence student's preference for online education. The purpose of this research is two fold: to investigate the number of attributes that are important for a student in opting for pursuing online course and to help universities marketing effort and the understanding of what determines a student's preference for online education. Conjoint analysis was used to investigate the number of attributes in prospective students in Ghana and Iran. The results indicate that the four most important determinants of preference for online course were course suitability, quality of teaching, quality of the course and library services which are important for education strategist to consider when developing and rolling out marketing campaigns and programmes. Keywords: Online education; Course selection; Conjoint analysis

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Sanchez, Mercedes, and Jose Gil. "A Conjoint Analysis of Quality Wine." Journal of Food Products Marketing 4, no. 2 (June 12, 1997): 63–78. http://dx.doi.org/10.1300/j038v04n02_05.

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Wittink, Dick R., and Philippe Cattin. "Commercial Use of Conjoint Analysis: An Update." Journal of Marketing 53, no. 3 (July 1989): 91. http://dx.doi.org/10.2307/1251345.

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Eggers, Felix, and Henrik Sattler. "Preference Measurement with Conjoint Analysis. Overview of State-of-the-Art Approaches and Recent Developments." GfK Marketing Intelligence Review 3, no. 1 (May 1, 2011): 36–47. http://dx.doi.org/10.2478/gfkmir-2014-0054.

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Abstract Determining consumer preferences is still one of the most important topics in marketing research. Not surprisingly, numerous approaches have been developed for this task. Conjoint measurement techniques are among the most prominent and different forms have emerged over the years. Depending on the specific research setting, all of them have their advantages and drawbacks. The authors discuss the nature and applicability of recent conjoint approaches and provide examples. Guidelines for selecting the optimal technique help to identify which approach works best in a given situation.

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Green, Paul E., and Kristiaan Helsen. "Cross-Validation Assessment of Alternatives to Individual-Level Conjoint Analysis: A Case Study." Journal of Marketing Research 26, no. 3 (August 1989): 346–50. http://dx.doi.org/10.1177/002224378902600308.

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Recently, both Hagerty and Kamakura have proposed insightful suggestions for improving the predictive accuracy of conjoint analysis via various types of averaging of individual responses. Hagerty uses Q-type factor analysis (i.e., optimal weighting) and Kamakura a hierarchical cluster analysis that optimizes predictive validity. Both approaches are compared with conventional conjoint and self-explicated utility models using real datasets. Neither the Hagerty nor the Kamakura suggestions lead to higher predictive validities than are obtained by conventional conjoint analysis applied to individual response data.

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Carmone, Frank. "ACA System for Adaptive Conjoint Analysis." Journal of Marketing Research 24, no. 3 (August 1987): 325. http://dx.doi.org/10.2307/3151649.

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Wittink, Dick R., and Jordan J. Louviere. "Analyzing Decision Making: Metric Conjoint Analysis." Journal of Marketing Research 26, no. 2 (May 1989): 244. http://dx.doi.org/10.2307/3172612.

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Green, Paul E. "Book Review: Consurv: Conjoint Analysis Software." Journal of Marketing Research 29, no. 3 (August 1992): 387–90. http://dx.doi.org/10.1177/002224379202900315.

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Hofstede, Frenkel Ter, Youngchan Kim, and Michel Wedel. "Bayesian Prediction in Hybrid Conjoint Analysis." Journal of Marketing Research 39, no. 2 (May 2002): 253–61. http://dx.doi.org/10.1509/jmkr.39.2.253.19087.

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The authors propose a general model that includes the effects of discrete and continuous heterogeneity as well as self-stated and derived attribute importance in hybrid conjoint studies. Rather than use the self-stated importances as prior information, as has been done in several previous approaches, the authors consider them data and therefore include them in the formulation of the likelihood, which helps investigate the relationship of self-stated and derived importances at the individual level. The authors formulate several special cases of the model and estimate them using the Gibbs sampler. The authors reanalyze Srinivasan and Park's (1997) data and show that the current model predicts real choices better than competing models do. The posterior credible intervals of the predictions of models with the different heterogeneity specifications overlap, so there is no clear superior specification of heterogeneity. However, when different sources of data are used—that is, full profile evaluations, self-stated importances, or both—clear differences arise in the accuracy of predictions. Moreover, the authors find that including the self-stated importances in the likelihood leads to much better predictions than does considering them prior information.

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Chinburapa, Vijit. "Predicting Prescribing Intention and Assessing Drug Attribute Importance Using Conjoint Analysis." Journal of Pharmaceutical Marketing & Management 3, no. 2 (February 13, 1989): 3–18. http://dx.doi.org/10.1300/j058v03n02_02.

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Otter, Thomas, Regina Tüchler, and Sylvia Frühwirth-Schnatter. "Capturing consumer heterogeneity in metric conjoint analysis using Bayesian mixture models." International Journal of Research in Marketing 21, no. 3 (September 2004): 285–97. http://dx.doi.org/10.1016/j.ijresmar.2003.11.002.

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Park, Chan Su. "The robustness of hierarchical Bayes conjoint analysis under alternative measurement scales." Journal of Business Research 57, no. 10 (October 2004): 1092–97. http://dx.doi.org/10.1016/s0148-2963(03)00039-0.

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Wang, Fa, Haifeng Wang, and Joung Hyung Cho. "Consumer Preference for Yogurt Packaging Design Using Conjoint Analysis." Sustainability 14, no. 6 (March 16, 2022): 3463. http://dx.doi.org/10.3390/su14063463.

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Abstract:

With the growing consumption market of yogurt products, the continuous innovation of packaging design has become the major means of enterprise marketing for fast-moving consumer goods. Because different combinations of packaging design elements affect the consumers in different ways, the contradiction between the product packaging design and consumer demand has impacted the further development of these products’ marketing. To explore the relationship between the constituent elements of yogurt packaging design and consumer preferences, four kinds of factors to the purchased products’ attributes were selected from the packaging design, including the graphics, packaging colors, packaging shapes and label texts. The consumers’ preferences for different attributes of yogurt packaging design were quantitatively evaluated by the Conjoint Analysis Method (CAM). Consumers showed the strongest preference for yogurt packaging shapes (39.017%), and were the most satisfied with the concrete graphics of cool colors (31.330%); the level combination of attributes most preferred by consumers is that of concrete graphics, cool colors, gable-top boxes and simple labels. The packaging design satisfying consumer preferences gave rise to positive purchase attitudes. Such research results facilitated the understanding of the consumption market and provided the theoretical support necessary for the development of yogurt packaging design to match the consumers’ preferences.

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Journal articles on the topic 'Conjoint analysis (Marketing)'

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